ML Env

Use casesVoice of customer

Tag every review and ticket with your own fixed label list, so you can finally count things

Applies your own fixed label list to every review, ticket or survey answer, turning free text into a column you can pivot and chart.

How it works
You define a codebook: a fixed list of labels, each with a one-line definition and an example ('delivery-late: parcel arrived after the promised date'). The small model applies it to every incoming review, ticket or survey answer, one at a time, outputting only labels from your list. The result is a spreadsheet column you can pivot, chart and trend in whatever tool you already use — the model does the reading, your spreadsheet does the analytics.
Data you need
The text items themselves — reviews, tickets, survey answers, which most shops already have in their helpdesk or shop-platform export — plus a codebook you write. The codebook is the real work: labelling is only as reliable as the definitions are crisp, which is why you validate it against a human-coded sample before trusting the output.
What to expect
Handles straightforward items well; weaker on ambiguous or multi-issue texts — a ticket that complains about both delivery and a refund needs a codebook that allows more than one label per item, or you lose half the signal. A vague label like 'quality issues' produces confident nonsense, so expect an iteration or two on the definitions before the counts are trustworthy. Always include an 'other/unclear' bucket so ambiguous items have an honest home instead of being forced into the wrong label.
Where people stay involved
A person hand-codes a sample of items first and compares them against the model's labels before trusting it at scale; disagreements usually reveal a fuzzy codebook definition to fix, not a model to swap. Periodic spot checks after that, especially when your products or policies change.

Which model, and what it costs to run

Qwen3-8B

This job runs in bulk rather than to a waiting person, so size is not the constraint — we take the strongest independent score on sticking to the document that we may serve freely and that fits on a single card.

Licence
Apache-2.0
Weights at 4-bit
5 GB
Context
32K tokens
Publisher
Alibaba (Qwen Team)

What the hardware costs

One 48 GB card holds it

Rent in the EU
$1.60/hrScaleway, Paris (PAR2)
Buy the card
$7,569new, one-off
Or rent it by the token
$0.04 / $0.04per M in / out · DeepInfra

Hardware only, third-party prices from 2026-07. The figure excludes the KV cache, which grows with context length and how many people use it at once — sized properly in a conversation, not guessed here. Renting by the token is cheaper up front; why our customers still self-host is below.

Sticking to the document

Vectara Hallucination Leaderboard · HHEM · 17 of 18 models measured

Measured on public documents, by a model acting as judge. Read it beside the answer rate: the lowest hallucination rates on this board belong to models that simply decline more often.

Phi-43.7% · answers 80.7% ⚠Llama 3.3 70B4.1% · answers 99.5%Gemma 3 12B4.4% · answers 97.4%Qwen3-8B4.8% · answers 99.9%Mistral Small 3.25.1% · answers 97.9%Granite 4.0 Small5.2% · answers 100%DeepSeek-V3.25.3% · answers 96.6%Qwen3-14B5.4% · answers 99.9%
Shorter is betterFree to serveConditions apply⚠ answered under 95%

Independent measurement · Vectara · board updated May 11, 2026

The API is cheaper per token. Here is why our customers don't use it.

We will not pretend otherwise: renting a model by the token from a serverless API costs less per million tokens than a card we run for you. We show that price on every use-case page. What it does not include is the part a shop with a customer database actually pays for.

  1. 01

    Your data never leaves hardware you can point at

    A serverless "we don't retain your data" is a clause in a contract. Running the model on a card in Amsterdam is a fact of architecture: your catalogue, tickets and customer records are never sent to a third party at all. For a GDPR audit, that is the difference between a promise and a floor plan.

  2. 02

    The price cannot move without your say-so

    A serverless rate card is somebody else's lever. The provider can raise the price, retire the model, or change the terms, and your cost moves with it. The same model on the same card costs the same next year — you own the number.

  3. 03

    The model cannot be taken away

    Hosted APIs deprecate models on their own schedule; the one you built on can be gone in a quarter. An open-weight model on your own hardware runs for as long as you keep the lights on. No vendor can end-of-life it out from under you.

And the price gap closes with volume: past a card you keep busy — very roughly four billion tokens a month — owning is cheaper outright, even before the three reasons above.

Getting a case like this one from a conversation to production takes about two months, and you can stop at the end of any phase.

Also in voice of customer

All 15 →

Next

Tell us what your team does by hand

Describe the process that takes the most time. We will say plainly whether a model is the right tool for it.